Forest plantations can be established in order to restore degraded areas. Acoustic tomography, which is of increasing importance in forest management, was used in the present study to obtain information for managing plantations of Cedrelinga cateniformis Ducke in the Peruvian Amazon. The species is valuable in the timber sector of Peru, but the core wood tends to deteriorate and develop cavities. The main objective of the study was to model wood deterioration in Cedrelinga cateniformis Ducke using the data obtained through acoustic tomography. Eight plantations of varying ages were analyzed using acoustic tomography in order to obtain indicators of wood deterioration. Biometric, climatic, and edaphic data (explanatory variables) were also measured in each plantation. The indicator variables and explanatory variables were compared and evaluated using correlation and principal component analysis. Wood deterioration was modelled using stepwise regression. The indicator variables differed significantly between plantations and were mainly correlated with the biometric variables (age and diameter at breast height). The models explained 81% of the variability of pith rot. The percentage rotten area was minimal in young plantations (1%), and the opposite was observed in mature trees (21.5 to 25.6%). The study findings provide valuable information, enabling foresters to determine the optimal age and diameter for felling Cedrelinga cateniformis in plantations in the Peruvian Amazon.

Avocado crop (Persea americana Mill.) is of great commercial importance due to its high profitability. However, it is being affected by various diseases and pests that affect yield and reduce fruit quality. The aim of this research was to develop methodologies for the evaluation of avocado plantations using different non-destructive technologies for rapid phenotyping and early detection of the incidence of diseases or damage due to stress in the stem. A plot of 0.7 ha. was evaluated, with a total of 44 individuals using Field-Map technology (dasometric and morphological characterization), RGB-multispectral images from Remotely Piloted Aircraft System (RPAS) (rapid phenotyping), while 15 individuals were evaluated using tomography (assessment of the internal state of the stem). The results with tomography indicated that there is a tree with wood rot of 14% with a lower acoustic speed with respect to the other trees evaluated. A high correlation was observed between the dasometric variables (r-Pearson from 0.63 to 0.98) estimated with Field-Map [crown base height, crown projection (m2 ) and total height] and with RPAS (height, perimeter and area). The vegetation indices do not have a direct correlation with the dasometric variables; five of the indices have a high contribution to variability except for the Normalized Difference Red Edge (NDRE). It can be concluded that the technologies used in this study would help to perform evaluations with a greater number of reliable and precise data with respect to the information obtained in a traditional way, while they can be replicated in commercial plots or research studies of different perennial crops, generating useful information for management decisions and crop evaluation.

The Prosopis sp. individuals known as carob trees are key species in the development of dry forest and recovery of degraded areas in the northern coast of Peru. The evaluation of plantations, calculation of aboveground forest biomass (AFB) and carbon stock represent an important role in forest management and climate change mitigation. This study evaluates monitoring methodologies using multispectral and LiDAR images coupled to a UAV, to validate them and generate models to estimate carbon stocks. Seven species of Prosopis sp. were evaluated with the conventional methodology and significant differences were found between species for dasometric characteristics and vegetation indices, as well as in the comparison with the data obtained with LiDAR. Models were selected to determine BAF and the association between the aerial carbon obtained with the models constituted by LiDAR data and vegetation indexes that presented significant correlations (p < 0.05), seven models were built for carbon prediction and the model that has as regressor variables the total height and crown area obtained from LiDAR, as well as the indexes CIgreen, GNDVI, RECI, LCI and NDVI (R² = 0.77) stands out. This confirms that the use of the LiDAR methodology with the vegetation indices allows a more practical estimation of the carbon stored in the plantation.

Las especies Retrophyllum rospigliosii (Pilg.) C. N. Page (Romerillo hembra) y Prumnopitys harmsiana (Pilg.) de Laub. (Romerillo macho) son coníferas nativas del Perú y forman parte importante de los bosques nublados del Departamento de Cajamarca. Sus maderas son solicitadas en el mercado local para obras de carpintería, debido a las propiedades físico mecánicas y buen acabado; sin embargo, información sobre la estructura microscópica es escasa. El presente trabajo tuvo como objetivo caracterizar y determinar las diferencias anatómicas de la madera de las dos especies y así ampliar el conocimiento de sus estructuras. Se obtuvieron muestras por método no destructivo, preparadas y estudiadas mediante la microtomía y maceración; cuya descripción se realizó utilizando la norma del IAWA. La principal característica cualitativa que permite diferenciarlas es la presencia de parénquima difuso en Retrophyllum rospigliosii y ausente en Prumnopitys harmsiana. Gimnospermas; romerillos; microtomía; punteaduras; parénquima axial

Ulcumano, which is native to South America, is an important conifer in Peru. Molecular studies are scarce, limiting modern breeding and appropriate conservation activities. Currently, molecular markers are widely employed to explore genetic structure and diversity parameters of plant species in a fast and precise manner. The objective of this study was to analyze the genetic diversity and population structure of ulcumano in Peru by using DNA-based molecular markers. Nine Randomly Amplified Polymorphic DNA (RAPD) markers were used, while 95 individuals of ulcumano were sampled from three departments of Peru. A total of 265 DNA fragments were manually scored, but 247 of them were kept after removing the non-polymorphic markers. Genetic distances were calculated using R software based on Provesti´s coefficient. A dendrogram was obtained using the UPGMA clustering algorithm, showing no clear clustering. The principal coordinate analysis agreed with two population structure analyses, demonstrating that ulcumano is contained within two clusters, (i) Junín + Pasco, and (ii) Cajamarca, while very few individuals are intermixed. Genetic diversity parameters were estimated considering the two groups (populations) identified by STRUCTURE software. Nei's genetic diversity estimate varied between 0.22 and 0.28, while Shannon index ranged from 3.43 to 4.16. Population divergence (Fst) between the two clusters revealed low genetic differentiation (0.064). AMOVA analysis revealed that 87.31 and 12.69% of the total genetic variation were found within populations and between individuals, respectively. To the best of our knowledge, this is the first molecular study in ulcumano in Peru, and provides valuable information for the genetic improvement and sustainable management of this conifer in the country.

Maize (Zea mays L.) is a fundamental cereal in global food security, but its vulnerability to water stress compromises its productivity and threatens food availability. This study analyzed the relationship between the crop water stress index (CWSI), obtained from thermal images captured by the Zenmuse H20T camera, and various vegetation indices derived from the MicaSense RedEdge-MX Dual. The analysis included machine learning (ML) models such as random forest (RF), k-nearest neighbors (KNN), and gradient boosting regression (GBR). The results showed that RF was the most accurate model for predicting CWSI in maize, with a coefficient of determination (R²) of 0.80, a root mean square error (RMSE) of 0.13, and a mean absolute error (MAE) of 0.09. KNN achieved an R² of 0.78, an RMSE of 0.13, and an MAE of 0.09, while GBR reached an R² of 0.79, an RMSE of 0.14, and an MAE of 0.10. The red band (668 nm) played a crucial role in RF (70.69%) and GBR (50.92%), whereas in KNN, the simple ratio (SR) index showed the highest importance (36.40%). These findings confirm the superiority of ML models over traditional regression approaches for estimating CWSI in maize. Despite the satisfactory results, the algorithms underestimated CWSI values derived from thermal images, which highlights the need to refine these models to improve their accuracy in future agricultural applications.